// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;

template<typename DataType, int DataLayout, typename IndexType>
static void
test_simple_concatenation(const Eigen::SyclDevice& sycl_device)
{
	IndexType leftDim1 = 2;
	IndexType leftDim2 = 3;
	IndexType leftDim3 = 1;
	Eigen::array<IndexType, 3> leftRange = { { leftDim1, leftDim2, leftDim3 } };
	IndexType rightDim1 = 2;
	IndexType rightDim2 = 3;
	IndexType rightDim3 = 1;
	Eigen::array<IndexType, 3> rightRange = { { rightDim1, rightDim2, rightDim3 } };

	// IndexType concatDim1 = 3;
	//	IndexType concatDim2 = 3;
	//	IndexType concatDim3 = 1;
	// Eigen::array<IndexType, 3> concatRange = {{concatDim1, concatDim2, concatDim3}};

	Tensor<DataType, 3, DataLayout, IndexType> left(leftRange);
	Tensor<DataType, 3, DataLayout, IndexType> right(rightRange);
	left.setRandom();
	right.setRandom();

	DataType* gpu_in1_data =
		static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_in2_data =
		static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize() * sizeof(DataType)));

	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
	sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(), (left.dimensions().TotalSize()) * sizeof(DataType));
	sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(), (right.dimensions().TotalSize()) * sizeof(DataType));
	///
	Tensor<DataType, 3, DataLayout, IndexType> concatenation1(leftDim1 + rightDim1, leftDim2, leftDim3);
	DataType* gpu_out_data1 =
		static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize() * sizeof(DataType)));
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out1(gpu_out_data1,
																				 concatenation1.dimensions());

	// concatenation = left.concatenate(right, 0);
	gpu_out1.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 0);
	sycl_device.memcpyDeviceToHost(
		concatenation1.data(), gpu_out_data1, (concatenation1.dimensions().TotalSize()) * sizeof(DataType));

	VERIFY_IS_EQUAL(concatenation1.dimension(0), 4);
	VERIFY_IS_EQUAL(concatenation1.dimension(1), 3);
	VERIFY_IS_EQUAL(concatenation1.dimension(2), 1);
	for (IndexType j = 0; j < 3; ++j) {
		for (IndexType i = 0; i < 2; ++i) {
			VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0));
		}
		for (IndexType i = 2; i < 4; ++i) {
			VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0));
		}
	}

	sycl_device.deallocate(gpu_out_data1);
	Tensor<DataType, 3, DataLayout, IndexType> concatenation2(leftDim1, leftDim2 + rightDim2, leftDim3);
	DataType* gpu_out_data2 =
		static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize() * sizeof(DataType)));
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out2(gpu_out_data2,
																				 concatenation2.dimensions());
	gpu_out2.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 1);
	sycl_device.memcpyDeviceToHost(
		concatenation2.data(), gpu_out_data2, (concatenation2.dimensions().TotalSize()) * sizeof(DataType));

	// concatenation = left.concatenate(right, 1);
	VERIFY_IS_EQUAL(concatenation2.dimension(0), 2);
	VERIFY_IS_EQUAL(concatenation2.dimension(1), 6);
	VERIFY_IS_EQUAL(concatenation2.dimension(2), 1);
	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0));
		}
		for (IndexType j = 3; j < 6; ++j) {
			VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0));
		}
	}
	sycl_device.deallocate(gpu_out_data2);
	Tensor<DataType, 3, DataLayout, IndexType> concatenation3(leftDim1, leftDim2, leftDim3 + rightDim3);
	DataType* gpu_out_data3 =
		static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize() * sizeof(DataType)));
	Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out3(gpu_out_data3,
																				 concatenation3.dimensions());
	gpu_out3.device(sycl_device) = gpu_in1.concatenate(gpu_in2, 2);
	sycl_device.memcpyDeviceToHost(
		concatenation3.data(), gpu_out_data3, (concatenation3.dimensions().TotalSize()) * sizeof(DataType));

	// concatenation = left.concatenate(right, 2);
	VERIFY_IS_EQUAL(concatenation3.dimension(0), 2);
	VERIFY_IS_EQUAL(concatenation3.dimension(1), 3);
	VERIFY_IS_EQUAL(concatenation3.dimension(2), 2);
	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0));
			VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0));
		}
	}
	sycl_device.deallocate(gpu_out_data3);
	sycl_device.deallocate(gpu_in1_data);
	sycl_device.deallocate(gpu_in2_data);
}
template<typename DataType, int DataLayout, typename IndexType>
static void
test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device)
{

	IndexType leftDim1 = 2;
	IndexType leftDim2 = 3;
	Eigen::array<IndexType, 2> leftRange = { { leftDim1, leftDim2 } };

	IndexType rightDim1 = 2;
	IndexType rightDim2 = 3;
	Eigen::array<IndexType, 2> rightRange = { { rightDim1, rightDim2 } };

	IndexType concatDim1 = 4;
	IndexType concatDim2 = 3;
	Eigen::array<IndexType, 2> resRange = { { concatDim1, concatDim2 } };

	Tensor<DataType, 2, DataLayout, IndexType> left(leftRange);
	Tensor<DataType, 2, DataLayout, IndexType> right(rightRange);
	Tensor<DataType, 2, DataLayout, IndexType> result(resRange);

	left.setRandom();
	right.setRandom();
	result.setRandom();

	DataType* gpu_in1_data =
		static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_in2_data =
		static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_out_data =
		static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize() * sizeof(DataType)));

	Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
	Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
	Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(gpu_out_data, resRange);

	sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(), (left.dimensions().TotalSize()) * sizeof(DataType));
	sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(), (right.dimensions().TotalSize()) * sizeof(DataType));
	sycl_device.memcpyHostToDevice(gpu_out_data, result.data(), (result.dimensions().TotalSize()) * sizeof(DataType));

	//  t1.concatenate(t2, 0) = result;
	gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) = gpu_out;
	sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data, (left.dimensions().TotalSize()) * sizeof(DataType));
	sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data, (right.dimensions().TotalSize()) * sizeof(DataType));

	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			VERIFY_IS_EQUAL(left(i, j), result(i, j));
			VERIFY_IS_EQUAL(right(i, j), result(i + 2, j));
		}
	}
	sycl_device.deallocate(gpu_in1_data);
	sycl_device.deallocate(gpu_in2_data);
	sycl_device.deallocate(gpu_out_data);
}

template<typename DataType, typename Dev_selector>
void
tensorConcat_perDevice(Dev_selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_simple_concatenation<DataType, RowMajor, int64_t>(sycl_device);
	test_simple_concatenation<DataType, ColMajor, int64_t>(sycl_device);
	test_concatenation_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_concatenation_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(tensorConcat_perDevice<float>(device));
	}
}
